The future of epidemiology: methodological challenges and multilevel inference

Area of Science:

• Epidemiology
• Public Health
• Genomics
• Biostatistics

Background:

• Historical debates questioned whether 'risk factor epidemiology' shifted focus from populations to individuals.
• Current understanding acknowledges both individual and population-level determinants of public health.
• Ecologic studies and traditional risk factor research established multifactorial disease causation.

Purpose of the Study:

• To advocate for multilevel epidemiology, integrating diverse levels of inference from genes to populations.
• To address current debates on incorporating genomic data into epidemiology and public health research.
• To propose a comprehensive strategy for understanding human disease etiology by studying determinants at multiple levels.

Main Methods:

• Combines hypothesis-driven research with data mining approaches.
• Investigates genomic variation at the individual level and its population-level maintenance.
• Employs multilevel statistical methods for analyzing complex health determinants.

Main Results:

• Individual-level studies have yielded hypotheses leading to reduced morbidity and mortality for major diseases.
• Genomic variation, while measured individually, is a population phenomenon requiring epidemiologic investigation.
• Multilevel epidemiology offers a more comprehensive approach to understanding disease etiology.

Conclusions:

• Multilevel epidemiology is essential for a holistic understanding of health and disease.
• The integration of genetic, individual, and population data is crucial for future public health advancements.
• Advancements in statistical methods and data mining will facilitate the adoption of multilevel epidemiology.